Comparative study of adaptive controller using MIT rules and Lyapunov method for MPPT standalone PV systems

Abstract

Abstract. The Photovoltaic Generator have a nonlinear characteristic function relating the intensity at the voltage I = f (U) and depend on the variation of solar irradiation and temperature, In addition, its point of operation depends directly on the load that it supplies. To fix this drawback, and to extract the maximum power available to the terminal of the generator, an adaptation stage is introduced between the generator and the load to couple the two elements as perfectly as possible. The adaptation stage is associated with a command called MPPT MPPT (Maximum Power Point Tracker) whose is used to force the PVG to operate at the MPP (Maximum Power Point) under variation of climatic conditions and load variation. This paper presents a comparative study between the adaptive controller for PV Systems using MIT rules and Lyapunov method to regulate the PV voltage. The Incremental Conductance (IC) algorithm is used to extract the maximum power from the PVG by calculating the voltage Vref , and the adaptive controller is used to regulate and track quickly the PV voltage. The two methods of the adaptive controller will be compared to prove their performance by using the PSIM tools and experimental test, and the mathematical model of step-up with PVG model will be presented.

Artificial Intelligent Maximum Power Point Controller based Hybrid Photovoltaic/Battery System

“…The photocurrent (Iph) is proportional to the intensity of incident light, while the diode current (Id) is modeled by the p-n junction diode's behavior. The series resistance (Rs) represents resistive losses in the cell's electrical connections and materials, representing the overall resistance in series with the diode and load [19,20].…”

PV array connected to the grid with the implementation of MPPT algorithms (INC, P&O and FL Method)

“…On the other hand, if it is smaller than the opposite of the conductance ( ), the duty ratio is increased. This process is repeated until reaching the point of maximum power [15]. The follow-up of the maximum power point requiring the procedure above is indicated in Figure 5.…”

Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems

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